Papers by Hong Huang
Sample Design Engineering: An Empirical Study on Designing Better Fine-Tuning Samples for Information Extraction with LLMs (2024.emnlp-industry)
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| Challenge: | Prompt Engineering (PE) is renowned for improving IE performance through prompt modifications, but the realm of sample design for downstream fine-tuning remains unexplored. |
| Approach: | They propose a methodical approach to enhancing LLMs’ post-tuning performance by refining input, output, and reasoning designs. |
| Outcome: | The proposed approach outperforms heuristic design strategies on three complex IE tasks with four additional LLMs. |
Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)
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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang
| Challenge: | Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target. |
| Approach: | They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success. |
| Outcome: | The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design. |
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)
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| Challenge: | Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression. |
| Approach: | They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression. |
| Outcome: | The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline. |
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi (2020.coling-main)
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| Challenge: | low-resource African languages are traditionally left behind because of the lack of well-annotated data and effective preprocessing. |
| Approach: | They propose two news datasets for multi-class classification of news articles in two low-resource African languages. |
| Outcome: | The proposed datasets show that training embeddings on the higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. |
Argumentation Mining on Essays at Multi Scales (2020.coling-main)
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| Challenge: | Argumentation mining on essays is a new task in natural language processing. |
| Approach: | They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text. |
| Outcome: | The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset. |
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
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Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
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| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)
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| Challenge: | Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation. |
| Approach: | They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them. |
| Outcome: | The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches. |
GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)
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| Challenge: | a funder name refers to an agency, organization, or program providing financial support for the research. |
| Approach: | They propose a funding sentence classifier and a relation extraction framework to extract grant information from scientific articles. |
| Outcome: | The proposed framework outperforms state-of-the-art BERT-based RE baselines against the PubMed Central and arXiv test sets. |
DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025.findings-naacl)
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Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
| Challenge: | Text-to-image (T2I) generation models have advanced in recent years, but effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation. |
| Approach: | They propose to use off-the-shelf MLLMs and T2I models to build a multi-modal interactive dialogue system (MIDS) that can generate correct output modalities and coherence of output images. |
| Outcome: | The proposed pipeline can generate correct output modalities and coherent multi-modal outputs compared with other state-of-the-art models. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction (2026.findings-acl)
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| Challenge: | Existing approaches to ground large language models in external knowledge are limited by hallucinations and a lack of granular medical context. |
| Approach: | They propose a framework that replaces external retrieval with internal, key-based knowledge access by encoding clinical information directly into the model’s parameter space. |
| Outcome: | The proposed framework achieves state-of-the-art performance across four benchmark healthcare outcome prediction datasets. |
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification (2020.coling-main)
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| Challenge: | inessential words are unintentionally misjudged as attention-worthy words and assigned heavier attention weights than should be. |
| Approach: | They propose a penalty-based method to regulate the attention learning process by integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. |
| Outcome: | The proposed method improves on the Penn Discourse TreeBank corpus and is competitive compared to the state-of-the-art methods. |
BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)
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| Challenge: | Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged. |
| Approach: | They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately. |
| Outcome: | The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters. |
Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)
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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently . |
| Approach: | They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model. |
| Outcome: | The proposed framework renders long texts into compact visual pages and processes them with a vision-language model. |
Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)
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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang
| Challenge: | Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon . |
| Approach: | They propose a routing-guided intervention method that enhances domain expert activation. |
| Outcome: | The proposed method achieves consistent improvements on visual reasoning tasks. |
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)
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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu
| Challenge: | Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains. |
| Approach: | They propose a training framework that operationalizes this principle through coarse-to-fine budgeting. |
| Outcome: | Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines. |
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification (2026.acl-long)
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| Challenge: | ternary quantization is a powerful solution for resource-constrained edge devices . current implementations suffer from a fundamental misalignment with commodity hardware . |
| Approach: | They propose a hardware-efficient ternary quantization framework that packs weights into five bits to restore power-of-two alignment. |
| Outcome: | The proposed framework reduces weights to -1, 0, +1 while preserving power-of-two alignment. |
Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer (2024.acl-srw)
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| Challenge: | Existing methods to translate spoken utterances from one language to another are unable to preserve speaker timbre of source speech. |
| Approach: | They propose a pipeline with style-transfer capability on the basis of self-supervised speech representations and codec units. |
| Outcome: | The proposed model achieves zero-shot cross-lingual style transfer on previously unseen source languages. |
Fingerprinting LLMs via Prompt Injection (2026.acl-long)
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Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li, Osama Ahmed, Zhicong Huang, Cheng Hong, Neil Zhenqiang Gong
| Challenge: | Existing provenance detection methods for large language models are infeasible for already published models and compare outputs using hand-crafted or random prompts. |
| Approach: | They propose a detection framework that constructs fingerprints by exploiting LLMs’ inherent vulnerability to prompt injection. |
| Outcome: | The proposed framework achieves high true positive rates while keeping false positive rates near zero. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards (2026.acl-long)
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Youliang Yuan, Qiuyang Mang, Jingbang Chen, Hong Wan, Xiaoyuan Liu, Junjielong Xu, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Pinjia He
| Challenge: | Existing models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. |
| Approach: | They propose a Rubric Reward Model that rewards the entire reasoning trajectory against problem-specific rubrics. |
| Outcome: | The proposed model outperforms outcome-only supervision on four math benchmarks and boosts Verified Pass@1024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%. |
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine (2026.findings-acl)
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Jiatan Huang, Mingchen Li, Zonghai Yao, Dawei Li, Yuxin Zhang, Zhichao Yang, Yongkang Xiao, Feiyun Ouyang, Xiaohan Li, Shuo Han, Hong yu
| Challenge: | Existing methods for retrieving medical textual knowledge Graphs struggle to perform well, a study finds . existing methods struggle to provide accurate answers to complex questions, he says . |
| Approach: | They synthesize user queries integrating diverse topological structures, relational information, and complex textual descriptions. |
| Outcome: | a new dataset for medical textual knowledge graphs shows that existing methods struggle to perform well . main bottlenecks lie in the scarcity of existing medical TKGs and the limited expressiveness of their topological structures . |
From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens (2025.emnlp-demos)
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Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi
| Challenge: | Existing vision-language models are based on exactmatch based accuracy and its derivations to evaluate performance. |
| Approach: | They propose a toolkit that supports systematic benchmarking, analysis, and interpretation of vision-language models by extracting intermediate outputs from any layer during the forward pass of open-source VLMs. |
| Outcome: | The proposed toolkit supports 16 state-of-the-art base VLMs and their over 30 variants and is extensible to accommodate new models without changing the core logic. |
Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy (2021.findings-emnlp)
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| Challenge: | Latent Dirichlet allocation (LDA) is a widely used topic model to discover the latent semantic of text data. |
| Approach: | They propose to combine a subsampling method with CGS to improve efficiency while amplifying privacy by using a novel metric, the efficiency–privacy function. |
| Outcome: | The proposed algorithm improves efficiency while amplifying privacy while subsampling in CGS increases efficiency while preserving privacy. |
How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities (2026.acl-long)
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Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng
| Challenge: | Large language models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors pose significant risks. |
| Approach: | They propose a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. |
| Outcome: | The proposed framework offers a principled and interpretable framework for safe and controllable LLM behavior serving as a foundation for future research. |
Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)
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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao
| Challenge: | Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment. |
| Approach: | VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control. |
| Outcome: | Experimental results show that VersBand performs better than baseline models across multiple song generation tasks. |
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)
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| Challenge: | Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy . |
| Approach: | They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio. |
| Outcome: | The proposed model outperforms existing models regardless of visibility of the scene . it can generate high-perceived audio, opening up new avenues for AR and VR applications . |
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)
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| Challenge: | Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs. |
| Approach: | They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models. |
| Outcome: | The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models. |
x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)
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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language. |
| Approach: | They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. |
| Outcome: | The proposed model can reason in a single dominant language on a per-instance basis. |
AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)
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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo
| Challenge: | Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. |
| Approach: | They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation. |
| Outcome: | The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts. |
Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are fundamentally reshaping the scientific landscape, transitioning the role of AI from passive tools to active partners within a new paradigm of Human-AI collaboration. |
| Approach: | They propose a benchmark to evaluate the ability of Large Language Models to improve papers with human feedback. |
| Outcome: | The proposed benchmark tests the skills of Large Language Models (LLMs) on paper interpretation, experimental implementation, and paper formulation, using authors’ camera-ready versions as natural human baselines. |
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
| Approach: | They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language. |
| Outcome: | The proposed method achieves favorable control ability and audio quality. |
Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning (2025.emnlp-main)
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Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang, Sirui Song, Yuhao Zhou, Xinbo Zhang, Peng Sun, Hong Lu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing work focuses on enabling models to generate natural language chain-of-thought rationales or leverage executable and verifiable code, such as Python. |
| Approach: | They propose a novel training pipeline that integrates sequential P-CoT and N-Co T generation and a subtask hybrid training strategy to facilitate natural language transferability. |
| Outcome: | The proposed training pipeline improves both N-CoT and P-Co T performance over the RL baseline. |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions (2026.acl-long)
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| Challenge: | Existing reinforcement learning methods are expensive due to high latency and sample inefficiency . Currently, RL is limited to one-to-one state-action pairs . |
| Approach: | They propose a framework that shifts the training paradigm to Single State Multiple Actions and introduce a group-wise advantage estimator based on the averaged critic outputs. |
| Outcome: | The proposed framework achieves 7.5% and 8.3% success rate improvements on AndroidLab and AndroidWorld over UI-TARS-1.5-7B and attains 1.4x higher training efficiency than existing methods. |
Self-Supervised Singing Voice Pre-Training towards Speech-to-Singing Conversion (2024.findings-acl)
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| Challenge: | Existing studies on speech-to-singing voice conversion (STS) are limited by the scarcity of paired speech-song data and the suboptimal quality of outputs. |
| Approach: | They propose a self-supervised singing voice pre-training model that transforms a speech-to-singing voice into a paired singing voice. |
| Outcome: | The proposed model improves both STS and singing voice synthesis tasks by combining spoken language and a self-supervised singing voice pre-training model. |
Linear-time Constituency Parsing with RNNs and Dynamic Programming (P18-2)
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| Challenge: | Existing span-based constituency parsers are too slow for longer sentences and for applications beyond sentence boundaries. |
| Approach: | They propose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search. |
| Outcome: | The proposed parser is faster for long sentences and faster for discourse parsing. |
Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data (2025.naacl-long)
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Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi
| Challenge: | Contrastive Language-Image Pre-training (CLIP) is a standard for cross-modal image-text representation learning. |
| Approach: | They propose a framework that enhances pre-trained CLIP models by exploiting challenging text-image pairs within existing datasets. |
| Outcome: | The proposed framework improves CLIP models by exploiting text-image pairs in training. |
The Model Agreed, But Didn’t Learn: Diagnosing Surface Compliance in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models internalize vast world knowledge as parametric memory, yet inherit the staleness and errors of their source corpora. |
| Approach: | They propose a framework that subjects models to discriminative self-assessment under diverse contextual pressures to scrutinize subtle behavioral nuances induced by memory modifications. |
| Outcome: | The proposed framework achieves high benchmarks without overwriting internal beliefs, while recursive modifications accumulate representational residues, triggering cognitive instability and permanently diminishing the reversibility of the model’s memory state. |
Robust Singing Voice Transcription Serves Synthesis (2024.acl-long)
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| Challenge: | Current AST methods struggle with accuracy and robustness when used for practical annotation. |
| Approach: | They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets. |
| Outcome: | The proposed model outperforms baseline models on enlarged, automatically annotated datasets. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
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| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
Text-to-Song: Towards Controllable Music Generation Incorporating Vocal and Accompaniment (2024.acl-long)
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Zhiqing Hong, Rongjie Huang, Xize Cheng, Yongqi Wang, Ruiqi Li, Fuming You, Zhou Zhao, Zhimeng Zhang
| Challenge: | Existing studies focus on singing voice synthesis and music generation independently. |
| Approach: | They propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation. |
| Outcome: | The proposed method can synthesize songs with comparable quality and style consistency. |
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)
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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang
| Challenge: | Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult. |
| Approach: | They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples. |
| Outcome: | The proposed approach improves preference while preserving utility. |
LoRACoE: Improving Large Language Model via Composition-based LoRA Expert (2025.emnlp-main)
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| Challenge: | Recent studies show that the Mixture of Experts architecture improves performance of large language models. |
| Approach: | They propose a method to build static experts using LoRA parameters . they propose to use rank-level parameters to build experts based on rank-based parameters based in LoRA module. |
| Outcome: | The proposed method improves task performance across a broader range of tasks. |
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure (2022.emnlp-main)
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| Challenge: | Current explanation datasets often employ synthetic data with simple reasoning structures. |
| Approach: | They propose a comprehensive logical reasoning explanation form that incorporates three main components to better fit the human cognitive process. |
| Outcome: | The proposed model performs better than existing models on real-life scenarios, but is more challenging for the current models. |
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis (2025.acl-long)
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| Challenge: | Existing methods for quantized fine-tuning fail to address activation outliers . existing methods incur high computational/memory costs or fail to adequately address outlier activation . |
| Approach: | They propose a Quantized parameter-efficient fine-tuning framework that suppresses outliers exclusively in invariant channels using lightweight operations. |
| Outcome: | The proposed framework reduces outliers in invariant channels while reducing quantization errors. |